Objective

To establish a nomogram for predicting the occurrence of postoperative delirium (POD) in patients undergoing cardiac surgery.

Materials and methods

Data from 5379 patients were retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) database and divided into a training set and a validation set at a 7:3 ratio. Multivariate logistic regression was conducted to identify independent predictors and establish nomograms to predict the occurrence of POD. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the accuracy and reliability of the model.

Results

A total of 5379 post-cardiac surgery patients were included in the study, with 258 patients in the training set and 113 patients in the validation set developing POD. Multivariate logistic regression analysis identified seven independent predictors: age, partial pressure of carbon dioxide (PCO2), glucose, white blood cell count (Wbc), stroke, anemia and chronic obstructive pulmonary disease (COPD). The prediction model demonstrated good discrimination, with an AUC of 0.702 (95 CI: 0.671–0.734) in the training set and 0.711 (95 CI: 0.7662 − 0.761) in the validation set. The calibration curve of the prediction model closely matched the ideal curve in both the training set and the validation set. In addition, the DCA curve demonstrated that the nomogram has better clinical applicability.

Conclusion

We constructed a nomogram for the personalized prediction of delirium in post-cardiac surgery patients, demonstrating satisfactory performance and clinical utility. This tool may help clinicians initiate preventive interventions for POD.